Assessment of growing condition variables on alfalfa productivity
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This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from −0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25℃ were over 0.88 in factor 1. The sunshine duration at temperature between 0℃–25℃ was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25℃ or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.
Abstract
This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from −0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25℃ were over 0.88 in factor 1. The sunshine duration at temperature between 0℃–25℃ was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25℃ or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.
참고문헌 (33)
- Kim DA, "Forage crop - its characteristics and management method", pp.91~126 (1983), Sunjinmunhwasa
- "Alfalfa stand establishment" (2019)[URL]
- Anderson B, "Seeding alfalfa" (2014), University of Nebraska
- Durling JC, "Predicting first-cut alfalfa yields from preceding winter weather", J Prod Agric, 8, pp.254~259 (1995)[DOI]
- Bourgeois G, "Evaluation of an alfalfa growth simulation model under Québec conditions", Agric Syst, 32, pp.1~12 (1990)[DOI]
- 김지융, "수량예측모델을 통한 Alfalfa 수량에 영향을 미치는 기후요인 및 토양요인의 기여도 평가", 한국초지조사료학회지, 41(1), pp.47~55 (2021), 한국초지조사료학회[DOI]
- Dhont C, "Untimely fall harvest affects dry matter yield and root organic reserves in field-grown alfalfa", Crop Sci, 44, pp.144~157 (2004)[DOI]
- Alfalfa production handbook, "Shroyer JP", Kansas State University, 12~16 (1987)
- Park BH, "Forage resource science", pp.137~143 (1997), Hyangmunsa
- Alfalfa irrigation and drought, "Shewmaker GE", College of Agricultural and Life Sciences, ~ (2013)
- John J, "Establishing alfalfa for forage. FSA15" (2021), University of Arkansas
- 김찬용, "밭작물 토양물리성 지표관련 인자의 상관분석", 한국토양비료학회지(Korean Journal of Soil Science and Fertilizer), 43(6), pp.793~797 (2010), 한국토양비료학회
- 김문주, "Causality between climatic and soil factors on Italian ryegrass yield in paddy field via climate and soil big data", 한국축산학회지, 61(6), pp.324~332 (2019), 한국축산학회[DOI]
- Wolf DD, "Leaf development of alfalfa at several temperatures", Crop Sci, 11, pp.479~482 (1971)[DOI]
- Sharratt BS, "Base temperature for the application of the growingdegree-day model to field-grown alfalfa", Field Crops Res, 21, pp.95~102 (1989)[DOI]
- Teuber L, "Alfalfa growth and development", Proceedings of the 18th California Alfalfa Symposium (1988)
- Matthews C, "Alfalfa response to heat stress is modulated by microRNA156", Physiol Plant, 165, pp.830~842 (2018)[DOI]
- Undersander D, "Alfalfa germination & growth (A3681)" (2011), University of Wisconsin-Extension, Cooperative Extension
- NICS, "Soil investigation theory and practical techniques", pp.40~74 (2003), Rural Development Administration
- Chamber JM, "Graphical methods for data analysis", pp.1983, Wadsworth and Brooks
- Brown BL, "Encyclopedia of statistics in behavioral science" (2005), John Wiley & Sons
- Johnson RA, "Applied multivariate statistical analysis" (2002), Prentice Hall
- "The R foundation" (2019)[URL]
- Kim CJ, "An introduction to grassland science", pp.143~179 (1995), Hyangmunsa
- Baron VS, "Forages: the science of grassland agriculture", pp.83~104 (2007), Blackwell Scientific
- "Weather data service-Open MET data portal" (2022)[URL]
- Kim DA, "Analyses on the seasonal productivity and summer depression of cool-season pasture crops", J Anim Sci Technol, 10, pp.97~104 (1968)
- Undersander D, "Alfalfa management guide" (2011), American Society of Agronomy
- Putnam DH, "Encyclopedia of agriculture and food systems", pp.381~405 (2014), Elsevier
- Saeed IAM, "Irrigation effects on the growth, yield, and water use efficiency of alfalfa", Irrig Sci, 17, pp.63~68 (1997)[DOI]
- Blad BL, "Evaluation of resistance and mass transport evapotranspiration models requiring canopy temperature data", Agron J, 68, pp.764~769 (1976)[DOI]
- Baral R, "Yield gap analysis of rainfed alfalfa in the United States", Front Plant Sci, 13, pp.931403~ (2022)[DOI]
- Alfalfa management in Georgia, "Hancock DW", University of Georgia, ~ (2009)